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Raffaele Marino
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2020 – today
- 2024
- [j4]Raffaele Marino
, Federico Ricci-Tersenghi
Phase transitions in the mini-batch size for sparse and dense two-layer neural networks. Mach. Learn. Sci. Technol. 5(1): 15015 (2024) - [i18]Carlo Nicolini, Jacopo Staiano, Bruno Lepri, Raffaele Marino:
The Garden of Forking Paths: Observing Dynamic Parameters Distribution in Large Language Models. CoRR abs/2403.08739 (2024) - [i17]Raffaele Marino, Lorenzo Buffoni, Bogdán Zaválnij:
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms. CoRR abs/2403.09742 (2024) - [i16]Lorenzo Chicchi, Lorenzo Buffoni, Diego Febbe, Lorenzo Giambagli, Raffaele Marino, Duccio Fanelli:
Automatic Input Feature Relevance via Spectral Neural Networks. CoRR abs/2406.01183 (2024) - [i15]Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi, Francesca Di Patti, Diego Febbe, Lorenzo Giambagli, Duccio Fanelli:
Learning in Wilson-Cowan model for metapopulation. CoRR abs/2406.16453 (2024) - [i14]Raffaele Marino:
Fast Analysis of the OpenAI O1-Preview Model in Solving Random K-SAT Problem: Does the LLM Solve the Problem Itself or Call an External SAT Solver? CoRR abs/2409.11232 (2024) - [i13]Lorenzo Chicchi, Duccio Fanelli, Diego Febbe, Lorenzo Buffoni, Francesca Di Patti, Lorenzo Giambagli, Raffaele Marino:
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise. CoRR abs/2409.13470 (2024) - 2023
- [j3]Raffaele Marino
Large Independent Sets on Random d-Regular Graphs with Fixed Degree d. Comput. 11(10): 206 (2023) - [j2]Raffaele Marino
Solving Non-linear Kolmogorov Equations in Large Dimensions by Using Deep Learning: A Numerical Comparison of Discretization Schemes. J. Sci. Comput. 94(1): 8 (2023) - [i12]Raffaele Marino
Phase transitions in the mini-batch size for sparse and dense neural networks. CoRR abs/2305.06435 (2023) - [i11]Maria Chiara Angelini, Angelo Giorgio Cavaliere, Raffaele Marino
Stochastic Gradient Descent-like relaxation is equivalent to Glauber dynamics in discrete optimization and inference problems. CoRR abs/2309.05337 (2023) - [i10]Raffaele Marino
Where do hard problems really exist? CoRR abs/2309.16253 (2023) - [i9]Raffaele Marino
A Bridge between Dynamical Systems and Machine Learning: Engineered Ordinary Differential Equations as Classification Algorithm (EODECA). CoRR abs/2311.10387 (2023) - [i8]Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, Raffaele Marino, Duccio Fanelli:
Complex Recurrent Spectral Network. CoRR abs/2312.07296 (2023) - 2022
- [i7]Raffaele Marino, Scott Kirkpatrick:
Hard Optimization Problems have Soft Edges. CoRR abs/2209.04824 (2022) - 2021
- [j1]Raffaele Marino
Learning from survey propagation: a neural network for MAX-E-3-SAT. Mach. Learn. Sci. Technol. 2(3): 35032 (2021) - [i6]Masoud Mohseni, Daniel Eppens, Johan Strümpfer, Raffaele Marino, Vasil S. Denchev, Alan K. Ho, Sergei V. Isakov, Sergio Boixo, Federico Ricci-Tersenghi, Hartmut Neven:
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization. CoRR abs/2111.13628 (2021) - 2020
- [i5]Raffaele Marino
Large independent sets on random d-regular graphs with d small. CoRR abs/2003.12293 (2020) - [i4]Raffaele Marino
Learning from Survey Propagation: a Neural Network for MAX-E-3-SAT. CoRR abs/2012.06344 (2020) - [i3]Nicolas Macris, Raffaele Marino
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes. CoRR abs/2012.07747 (2020)
2010 – 2019
- 2018
- [i2]Raffaele Marino
Revisiting the Challenges of MaxClique. CoRR abs/1807.09091 (2018) - 2015
- [i1]Raffaele Marino, Giorgio Parisi, Federico Ricci-Tersenghi:
The Backtracking Survey Propagation Algorithm for Solving Random K-SAT Problems. CoRR abs/1508.05117 (2015)
Coauthor Index
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last updated on 2024-10-18 19:32 CEST by the dblp team
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